Method and system for predicting human congnitive performance

ABSTRACT

An apparatus and method for predicting cognitive performance of an individual based on factors including preferably sleep history and the time of day. The method facilitates the creation of predicted cognitive performance curves that allow an individual to set his/her sleep times to produce higher levels of cognitive performance. The method also facilitates the reconstruction of past cognitive performance levels based on sleep history.

This application is a continuation of U.S. application Ser. No.09/844,434, filed on Apr. 30, 2001, now U.S. Pat. No. 6,530,884 which isa continuation-in-part of PCT Application No. PCT/US99/20092, filed Sep.3, 1999 (which designates the United States and was published on May 11,2000), which claims priority from U.S. provisional Application SerialNo. 60/106,419, filed Oct. 30, 1998, and U.S. provisional ApplicationSerial No. 60/122,407, filed Mar. 2, 1999; and U.S. application Ser. No.09/844,434 claims the benefit of U.S. provisional Application Serial No.60/273,540, filed Mar. 7, 2001. These patent applications are herebyincorporated by reference.

FIELD OF THE INVENTION

This invention relates to a method for predicting cognitive performanceof an individual preferably based on that individual's prior sleep/wakehistory, the time of day, and tasks (or activities) being performed bythe individual.

BACKGROUND OF THE INVENTION

Maintenance of productivity in any workplace setting depends uponeffective cognitive performance at all levels from command/control ormanagement down to the individual soldier or worker. Effective cognitiveperformance in turn depends upon complex mental operations. Many factorshave been shown to affect cognitive performance (e.g., drugs or age).However, of the numerous factors causing day to day variations incognitive performance, two have been shown to have the greatest impact.These two factors are an individual's prior sleep/wake history and thetime of day.

Adequate sleep sustains cognitive performance. With less than adequatesleep, cognitive performance degrades over time. An article by Thorne etal. entitled “Plumbing Human Performance Limits During 72 hours of HighTask Load” in Proceedings of the 24^(th) DRG Seminar on the Human as aLimiting Element in Military Systems, Defense and Civil Institute ofEnvironmental Medicine, pp. 17-40 (1983), an article by Newhouse et al.entitled “The Effects of d-Amphetamine on Arousal, Cognition, and MoodAfter Prolonged Total Sleep Deprivation” published inNeuropsychopharmacology, vol. 2, pp. 153-164 (1989), and another articleby Newhouse et al. entitled “Stimulant Drug Effects on Performance andBehavior After Prolonged Sleep Deprivation: A Comparison of Amphetamine,Nicotine, and Deprenyl” published in Military Psychology, vol. 4, pp.207-233 (1992) all describe studies of normal volunteers in which it isrevealed that robust, cumulative decrements in cognitive performanceoccur during continuous total sleep deprivation as measured bycomputer-based testing and complex operational simulation. In the Dingeset al. article entitled “Cumulative Sleepiness, Mood Disturbance, andPsychomotor Vigilance Performance Decrements During a Week of SleepRestricted to 4-5 Hours Per Night” published in Sleep, vol. 20, pp.267-277 (1997), it is revealed that on fixed, restricted daily sleepamounts, cumulative reduced sleep also leads to a cognitive performancedecline. Thus, in operational settings, both civilian and military,sleep deprivation reduces productivity (output of useful work per unitof time) on cognitive tasks.

Thus, using computer-based cognitive performance tests, it has beenshown that total sleep deprivation degrades human cognitive performanceby approximately 25% for each successive period of 24 hours awake.However, it also has been shown that even small amounts of sleep reducethe rate of sleep loss-induced cognitive performance degradation.Belenky et al. in their article entitled “Sustaining Performance DuringContinuous Operations: The U.S. Army's Sleep Management System,”published in 20^(th) Army Science Conference Proceedings, vol. 2, pp.657-661 (1996) disclose that a single 30-minute nap every 24 hoursreduces the rate of cognitive performance degradation to 17% per dayover 85 hours of sleep deprivation. This suggests that recuperation ofcognitive performance during sleep accrues most rapidly early in thesleep period. No other factor besides the amount of sleep contributes sosubstantially and consistently to the normal, daily variations incognitive performance.

In addition to sleep/wake history, an individual's cognitive performanceat a given point in time is determined by the time of day. In the early1950s, Franz Halberg and associates observed a 24-hour periodicity in ahost of human physiologic (including body temperature and activity),hematologic, and hormonal functions, and coined the term ‘circadian’(Latin for ‘about a day’) to describe this cyclic rhythm. Halberg showedthat most noise in experimental data came from comparisons of datasampled at different times of day.

When humans follow a nocturnal sleep/diurnal wake schedule (for example,an 8-hour sleep/16-hour wake cycle, with nightly sleep commencing atapproximately midnight), body temperature reaches a minimum (trough)usually between 2:00 AM and 6:00 AM. Body temperature then begins risingto a maximum (peak) usually between 8:00 PM and 10:00 PM. Likewise,systematic studies of daily human cognitive performance rhythms showthat speed of responding slowly improves across the day to reach amaximum in the evening (usually between 8:00 PM and 10:00 PM) thendropping more rapidly to a minimum occurring in the early morning hours(usually between 2:00 AM and 6:00 AM). Similar but somewhat lessconsistent rhythms have been shown from testing based on variouscognitive performance tasks. Thus, superimposed on the effect of totalsleep deprivation on cognitive performance noted above was anapproximately ±10% variation in cognitive performance over each 24-hourperiod.

Various measures have been shown to correlate, to some extent, withcognitive performance. These include objective and subjective measuresof sleepiness (or its converse, alertness). Some individuals familiarwith the art use “sleepiness” to indicate the opposite of “alertness”(as is the case in the present document). “Drowsiness” often is usedinterchangeably with “sleepiness” although some familiar with the artwould argue that “sleepiness” pertains specifically to the physiologicalneed for sleep whereas “drowsiness” refers more to the propensity orability to fall asleep (independent of physiological sleep need) or thesubjective feeling of lack of alertness. The term “fatigue” has beenused as a synonym for “sleepiness” by the lay population, but thosefamiliar with the art do not consider “fatigue” to be interchangeablewith “sleepiness”—rather, “fatigue” is a broad term that encompassesmore than just the effects of sleep loss per se on performance.Likewise, “cognitive performance” has been defined as performance on awide variety of tasks, the most commonly used being vigilance tasks(tasks requiring sustained attention). From vigilance and other tasks,some researchers use accuracy as their measure of cognitive performance,while others use reaction time (or its inverse, speed). Still others usea measure that is calculated as speed multiplied by accuracy, that isthe amount of useful work performed per unit of time (also known asthroughput). Those familiar with the art generally agree that vigilancetasks are appropriate measures of cognitive performance under conditionsof sleep deprivation, and that either reaction time (speed) or somemeasure that takes reaction time into account (e.g., throughput) is avalid and reliable way of measuring cognitive performance.

The Multiple Sleep Latency Test (MSLT) is a widely accepted objectivemeasure of sleepiness/alertness. In the MSLT, individuals try to fallasleep while lying in a darkened, quiet bedroom. Various physiologicalmeasures used to determine sleep or wakefulness are recorded (eyemovements, brain activity, muscle tone), and time taken to reach thefirst 30 seconds of stage 1 (light) sleep is determined. Shorterlatencies to stage 1 are considered to indicate greater sleepiness(lower alertness). Sleep latencies under 5 minutes are considered to bepathological (i.e., indicative of a sleep disorder or sleepdeprivation). During both total and partial sleep deprivation, latencyto sleep on the MSLT (alertness) and performance decline (i.e.,sleepiness as measured by MSLT increases). However, although there is acorrelation between MSLT-determined sleepiness/alertness and cognitiveperformance (greater sleepiness as indexed by MSLT corresponding topoorer cognitive performance), this correlation has never been shown tobe perfect and for the most part is not strong. As a result, the MSLT isa poor (i.e., unreliable) predictor of cognitive performance.

Subjective measures of sleepiness/alertness also have been shown tocorrelate (albeit weakly) with cognitive performance. Hoddes et al. intheir article entitled “Quantification of Sleepiness: A New Approach”published in Psychophysiology, vol. 10, pp. 431-436 (1973) describe theStanford Sleepiness Scale (SSS), a subjective questionnaire used widelyto measure sleepiness/alertness. In the SSS, individuals rate theircurrent level of sleepiness/alertness on a scale from 1 to 7, with 1corresponding to the statement, “feeling active and vital; alert; wideawake” and 7 corresponding to the statement “almost in reverie; sleeponset soon; losing struggle to remain awake.” Higher SSS scores indicategreater sleepiness. As with the MSLT, during both total and partialsleep deprivation, scores on the SSS increase. However, as with MSLT,the correspondence between SSS-determined sleepiness/alertness andcognitive performance decrements is weak and inconsistent. As a result,the SSS also is a poor predictor of cognitive performance. Some otherexamples of subjective measures of sleepiness/alertness include theEpworth Sleepiness Scale described by Johns in his article entitled“Daytime Sleepiness, Snoring, and Obstructive Sleep Apnea” published inChest, vol. 103, pp. 30-36 (1993) and the Karolinska Sleepiness scaledescribed by Akerstedt and Gillberg in their article entitled“Subjective and Objective Sleepiness in the Active Individual” publishedin International Journal of Neuroscience, vol. 52, pp. 29-37 (1990). Thecorrespondence between these subjective measures and cognitiveperformance also is weak and inconsistent.

In addition, factors modifying cognitive performance may notcorrespondingly affect objective or subjective measures ofsleepiness/alertness, and vice versa. For example, the Penetar et al.article entitled “Amphetamine Effects on Recovery Sleep Following TotalSleep Deprivation” published in Human Psychopharmacology, vol. 6, pp.319-323 (1991) discloses that during sleep deprivation, the stimulantdrug d-amphetamine improved cognitive performance but notsleepiness/alertness (as measured by the MSLT). In a similar study,caffeine given as a sleep deprivation countermeasure maintained elevatedcognitive performance for over 12 hours while the effects on subjectivesleepiness, vigor and fatigue transiently improved but then decayed.Thorne et al. in their article entitled “Plumbing Human PerformanceLimits During 72 hours of High Task Load” in Proceedings of the 24^(th)DRG Seminar on the Human as a Limiting Element in Military Systems,Defense and Civil Institute of Environmental Medicine, pp. 17-40 (1983)describe how cognitive performance continues to decline over 72 hours ofsleep deprivation whereas subjective sleepiness/alertness declined overthe first 24 hours but subsequently leveled off. The findings thatcognitive performance and measures of sleepiness/alertness are notalways affected in the same way indicate that they are notinterchangeable. That is, measures of sleepiness/alertness cannot beused to predict cognitive performance, and vice versa.

Methods and apparatuses related to alertness detection fall into fivebasic categories: a method/apparatus for unobtrusively monitoringcurrent alertness level; a method/apparatus for unobtrusively monitoringcurrent alertness level and providing a warning/alarm to the user ofdecreased alertness and/or to increase user's alertness level; amethod/apparatus for monitoring current alertness level based on theuser's responses to some secondary task possibly with an alarm device towarn the user of decreased alertness and/or to increase user's alertnesslevel; methods to increase alertness; and a method/apparatus forpredicting past, current, or future alertness.

These methods and apparatuses that unobtrusively monitor the currentalertness level are based on an “embedded measures” approach. That is,such methods infer alertness/drowsiness from the current level of somefactor (e.g., eye position or closure) assumed to correlate withalertness/drowsiness. Issued patents of this type include U.S. Pat. No.5,689,241 to J. Clarke, Sr., et al. disclosing an apparatus to detecteye closure and ambient temperature around the nose and mouth; U.S. Pat.No. 5,682,144 to K. Mannik disclosing an apparatus to detect eyeclosure; and U.S. Pat. No. 5,570,698 to C. Liang et al. disclosing anapparatus to monitor eye localization and motion to detect sleepiness.An obvious disadvantage of these types of methods and apparatuses isthat the measures are likely detecting sleep onset itself rather thansmall decreases in alertness.

In some patents, methods for embedded monitoring of alertness/drowsinessare combined with additional methods for signaling the user of decreasedalertness and/or increasing alertness. Issued patents of this typeinclude U.S. Pat. No. 5,691,693 to P. Kithil describing a device thatsenses a vehicle operator's head position and motion to compare currentdata to profiles of “normal” head motion and “impaired” head motion.Warning devices are activated when head motion deviates from the“normal” in some predetermined way. U.S. Pat. No. 5,585,785 to R. Gwinet al. describes an apparatus and a method for measuring total handgrippressure on a steering wheel such that an alarm is sounded when the grippressure falls below a predetermined “lower limit” indicatingdrowsiness. U.S. Pat. No. 5,568,127 to H. Bang describes a device fordetecting drowsiness as indicated by the user's chin contacting an alarmdevice, which then produces a tactile and auditory warning. U.S. Pat.No. 5,566,067 to J. Hobson et al. describes a method and an apparatus todetect eyelid movements. A change in detected eyelid movements from apredetermined threshold causes an output signal/alarm (preferablyauditory). As with the first category of methods and apparatuses, adisadvantage here is that the measures are likely detecting sleep onsetitself rather than small decreases in alertness.

Other alertness/drowsiness monitoring devices have been developed basedon a “primary/secondary task” approach. For example, U.S. Pat. No.5,595,488 to E. Gozlan et al. describes an apparatus and a method forpresenting auditory, visual, or tactile stimuli to an individual towhich the individual must respond (secondary task) while performing theprimary task of interest (e.g., driving). Responses on the secondarytask are compared to baseline “alert” levels for responding. U.S. Pat.No. 5,259,390 to A. MacLean describes a device in which the userresponds to a relatively innocuous vibrating stimulus. The speed torespond to the stimulus is used as a measure of the alertness level. Adisadvantage here is that the apparatus requires responses to asecondary task to infer alertness, thereby altering and possiblyinterfering with the primary task.

Other methods exist solely for increasing alertness, depending upon theuser to self-evaluate alertness level and activate the device when theuser feels drowsy. An example of the latter is U.S. Pat. No. 5,647,633and related patents to M. Fukuoka in which a method/apparatus isdescribed for causing the user's seat to vibrate when the user detectsdrowsiness. Obvious disadvantages of such devices are that the user mustbe able to accurately self-assess his/her current level of alertness,and that the user must be able to correctly act upon this assessment.

Methods also exist to predict alertness level based on user inputs knownempirically to modify alertness. U.S. Pat. No. 5,433,223 to M. Moore-Edeet al. describes a method for predicting the likely alertness level ofan individual at a specific point in time (past, current or future)based upon a mathematical computation of a variety of factors (referredto as “real-world” factors) that bear some relationship to alterationsin alertness. The individual's Baseline Alertness Curve (BAC) is firstdetermined based on five inputs and represents the optimal alertnesscurve displayed in a stable environment. Next, the BAC is modified byalertness modifying stimuli to arrive at a Modified Baseline AlertnessCurve. Thus, the method is a means for predicting an individual'salertness level, not cognitive performance.

Another method has been designed to predict “work-related fatigue” as afunction of number of hours on duty. Fletcher and Dawson describe theirmethod in an article entitled “A Predictive Model of Work-RelatedFatigue Based on Hours of Work” published in Journal of OccupationalHealth and Safety, vol. 13, 471-485 (1997). In this model a simplifyingassumption is made—it is assumed that length of on-duty time correlatespositively with time awake. To implement the method, the user inputs areal or hypothetical on-duty/off-duty (work/rest) schedule. Output fromthe model is a score that indicates “work-related fatigue.” Althoughthis “work-related fatigue” score has been shown to correlate with someperformance measures, it is not a direct measure of cognitiveperformance per se. It can be appreciated that the fatigue score will beless accurate under circumstances when the presumed relationship betweenon-duty time and time awake breaks down—for example when a person worksa short shift but then spends time working on projects at home ratherthan sleeping or when a person works long shifts but conscientiouslysleeps all the available time at home. Also, this method is obtrusive inthat the user must input on-duty/off-duty information rather than suchinformation being automatically extracted from an unobtrusive recordingdevice. In addition, the model is limited to predictions of “fatigue”based on work hours. Overall, this model is limited to work-relatedsituations in which shift length consistently correlates (inversely)with sleep length.

Given the importance of the amount of sleep and the time of day fordetermining cognitive performance (and hence estimating productivity oreffectiveness), and given the ever-increasing requirements of mostoccupations on cognitive performance, it is desirable to design areliable and accurate method of predicting cognitive performance. It canbe appreciated that increasing the number of relevant inputs increasescognitive performance prediction accuracy. However, the relativebenefits gained from such inputs must be weighed against the additionalburdens/costs associated with their collection and input. For example,although certain fragrances have been shown to have alertness-enhancingproperties, these effects are inconsistent and negligible compared tothe robust effects of the individual's sleep/wake history and the timeof day. More important, the effect of fragrances on cognitiveperformance is unknown. Requiring an individual to keep a log ofexposure to fragrances would be time consuming to the individual andonly result in negligible gains in cognitive performance predictionaccuracy. In addition, while the effects of the sleep/wake history andthe time of day on cognitive performance are well known, the effects ofother putative alertness-altering factors (e.g., job stress), how tomeasure them (their operational definition), and their direction ofaction (cognitive performance enhancing or degrading) are virtuallyunknown.

An important and critical distinction between the present invention andthe prior art is that the present invention is a model to predictperformance on tasks with a cognitive component. In contrast, previousmodels involving sleep and/or circadian rhythms (approximately 24-hour)focused on the prediction of “alertness” or “sleepiness.” The latter areconcepts that specifically relate to the propensity to initiate sleep,not the ability to perform a cognitive task.

Although sleepiness (or its converse, alertness) could be viewed as anintervening variable that can mediate cognitive performance, thescientific literature clearly shows that cognitive performance andalertness are conceptually distinct, as reviewed by Johns in the articleentitled “Rethinking the Assessment of Sleepiness” published in SleepMedicine Reviews, vol. 2, pp. 3-15 (1998) and as reviewed by Mitler etal. in the article entitled “Methods of Testing for Sleepiness”published in Behavioral Medicine, vol. 21, pp. 171-183 (1996). Thomas etal. in the article entitled “Regional Cerebral Metabolic Effects ofProlonged Sleep Deprivation” published in NeuroImage, vol. 7, p. S130(1998) reveal that 1-3 days of sleep loss result in reductions in globalbrain activation of approximately 6%, as measured by regional cerebralglucose uptake. However, those regions (heteromodal associationcortices) that mediate the highest order cognitive functions (includingbut not limited to attention, vigilance, situational awareness,planning, judgment, and decision making) are selectively deactivated bysleep loss to a much greater extent—up to 50%—after three days of sleeploss. Thus, decreases in neurobiological functioning during sleeprestriction/deprivation are directly reflected in cognitive performancedegradation. These findings are consistent with studies demonstratingthat tasks requiring higher-order cognitive functions, especially thosetasks requiring attention, planning, etc. (abilities mediated byheteromodal association areas) are especially sensitive to sleep loss.On the other hand, brain regions such as primary sensory regions, aredeactivated to a lesser degree. Concomitantly, performance (e.g.,vision, hearing, strength and endurance tasks) that is dependent onthese regions is virtually unaffected by sleep loss.

Consequently, devices or inventions that predict “alertness” per se(e.g., Moore-Ede et al.) putatively quantify the brain's underlyingpropensity to initiate sleep at any given point in time. That is,devices or inventions that predict “alertness” (or its converse“sleepiness”) predict the extent to which sleep onset is likely. Thepresent invention differs from such approaches in that the nature of thetask is accounted for i.e., it is not the propensity to initiate sleepthat is predicted. Rather, the present invention predicts the extent towhich performance of a particular task will be impaired by virtue of itsreliance upon brain areas most affected by sleep deprivation(heteromodal association areas of the brain). The most desirable methodwill produce a highly reliable and accurate cognitive performanceestimate based on the sleep/wake history of an individual, the time ofday, and the amount of time on a particular task.

SUMMARY OF THE INVENTION

A method for providing a cognitive performance level in accordance withthe invention includes receiving a data series representing at least onewake state and at least one sleep state, selecting a function based onthe data series, determining a cognitive performance capacity using theselected function, modulating the cognitive performance capacity with atime of day value, and providing the modulated value.

An apparatus for providing a cognitive performance level in accordancewith the invention includes means for receiving a data series having atleast one wake state and at least one sleep state, means for selecting afunction based on the data series, means for determining a cognitiveperformance capacity using the selected function, means for storing aseries of time of day values, means for modulating the cognitiveperformance capacity with a corresponding time of day value, and meansfor providing the modulated value.

A method for determining a cognitive performance level in accordancewith the invention includes inputting a data series having wake statesand sleep states of an individual, selecting a function based on thewake states and sleep states in the data series, calculating a cognitiveperformance capacity based on the selected function, modulating thecognitive performance capacity with a time of day value, and outputtingthe modulated value as the predicted cognitive performance.

A method for determining a predicted cognitive performance in accordancewith the invention includes inputting a data series having wake statesand sleep states of an individual, selecting a function based on thewake states and sleep states in the data series, calculating a cognitiveperformance capacity based on the selected function, approximating afirst curve of calculated cognitive performance capacities, modulatingthe first curve with a second curve representing time of day rhythms,and outputting the modulated first curve representing the predictedcognitive performance.

A feature of the present invention is that it provides a numericalrepresentation of predicted cognitive performance with an immediateergonomic and economic advantage, i.e., an indication of productivity oreffectiveness of an individual. Another feature of the present inventionis that it does not require or use measurements/computations that areindirect, intermediate, inferential or hypothetical concomitants ofcognitive performance. Examples of the latter are alertness, sleepiness,time to sleep onset, body temperature and/or other physiologicalmeasures that vary with time. A further feature of the invention is thatit accounts for transient or adventitious variations in cognitiveperformance from any source as a result of how that source affects thesleep/wake history (e.g., age) and/or physiological time of day (e.g.,shift work). In effect, such sources are not treated as having effectson cognitive performance independent of the sleep/wake history and/orthe time of day, and as such do not require separate measurement,tabulation, and input into the method.

One objective of this invention is to provide an accurate method forpredicting cognitive performance of an individual.

A further objective is to provide a method that facilitates predictionof the effects of possible future sleep/wake histories on cognitiveperformance (forward prediction).

Another objective is to provide a method that facilitates retrospectiveanalysis of likely prior cognitive performance based on, for example,the individual's sleep/wake history, the time of day, and the activitiesdone by the individual.

Another objective is to provide a method for coordination andoptimization of available sleep/wake time in order to obtain net optimalpredicted cognitive performance for an individual and/or a group ofindividuals.

It can be appreciated that an implicit advantage and novelty of themethod is its parsimony. The method uses those factors possessingmaximal predictive value (as demonstrated empirically) as continuouslyupdated inputs. Thus, the model will be simple to implement. Othermodels predicting “alertness” require the user to track multiple inputvariables (e.g., caffeine, alcohol ingestion, light/dark exposure,diurnal type), rather than presenting these inputs as optional“attachments” to a standard, simplified model based on those factorsaccounting for maximum cognitive performance change. For example, inaccordance with a segment of the present method, the effects of age oncognitive performance are accounted for implicitly via the empiricallyderived effects of age on sleep. That is, sleep quality degrades withage. The inherent degradation in sleep quality with aging wouldimplicitly result in a prediction of degraded cognitive performance(since in the present method degraded sleep results in a prediction ofdegraded cognitive performance), even if an individual's age wereunknown. Therefore, age need not constitute a separate (independent)input variable to a cognitive performance prediction model.

The invention also provides other significant advantages. For example,an advantage of this invention is the elimination of a need forempirical evaluation.

Another advantage of this invention is obtaining an accurate predictionof cognitive performance of an individual. The advantage may be achievedby a method incorporating, for example, at least two of the followingfactors that have been empirically demonstrated to exert a significanteffect on cognitive performance, namely, (1) the individual's sleep/wakehistory, (2) the time of day (“day” herein referring to a 24-hour periodincluding both nighttime and daylight hours), and (3) the individual'stime on a particular task.

Another advantage achieved by this invention is an accurate predictionof current cognitive performance.

Another advantage achieved by this invention is that it is capable ofproviding a real time prediction of cognitive performance.

Yet another advantage achieved by this invention is a prediction offuture expected cognitive performance throughout the day based onhypothetical future sleep/wake periods.

An additional advantage achieved by this invention is a retrospectiveanalysis of cognitive performance at given times.

A further advantage of the invention is that a particular cognitiveperformance prediction is not based on normative data (i.e., does notrequire a “look-up table” for output), but rather is calculated directlybased on, for example as discussed in connection with one embodiment,each individual's sleep/wake information, the time of day, and the timeon a task.

A further advantage of the invention is that it can be used to optimizethe individual's future sleep/wake schedule based on a fixedmission/work schedule. Previous methods and apparatuses are directedtoward modifying the work schedule and/or mission to “fit theindividual.” In most situations, however, work schedules and/or missionsare fixed. Thus, modifying the work schedule or mission to suit theindividual is impractical or impossible. A more reasonable approachincorporated in the present method is to allow the individual to adjusthis/her sleep/wake periods to meet work/mission demands. Thus, thecurrent method presents a more practical alternative by providing ameans to regulate work hours to a directly applicable metric (cognitiveperformance) instead of regulating work hours by time off duty or byusing indirect measures of cognitive performance such as alertness.

A feature of this invention is the provision of a graphicalrepresentation that translates an individual's sleep/wake history andthe time of day into an immediately useful, self-evident index. Aprediction of cognitive performance, unlike a prediction of “alertness”or “sleepiness,” requires no further interpretation.

The method for predicting human cognitive performance in accordance withthe invention accomplishes the above objectives and achieves the aboveadvantages. The method and resulting apparatus are easily adapted to awide variety of situations and types of inputs.

In accordance with an aspect of the invention, an individual sleep/wakehistory is inputted into a processing device. The processing deviceclassifies the individual pieces of sleep/wake history data as eithersleep or wake. Based on the classification of data, the processingdevice selects and calculates a cognitive performance capacitycorresponding to the present state of the individual, the cognitiveperformance capacity may be modified by a time of day value to adjustthe cognitive performance capacity to a predicted cognitive performance.The predicted cognitive performance represents the ability of theindividual to perform cognitive tasks. The predicted cognitiveperformance may be displayed for a real-time indication or as part of acurve, printed out with the information that could have been displayed,and/or stored for later retrieval and/or use. The calculation of thecognitive performance capacity is made based on functions that model theeffect of the interrelationship of sleep and being awake on cognitiveperformance. The time of day function models the effect of anindividual's circadian rhythms on cognitive performance.

In accordance with the underlying method of the invention, the methodcan be accomplished with a wide variety of apparatus. Examples of thepossible apparatus embodiments include electronic hardware as eitherdedicated equipment or equipment internal to a computer, softwareembodied in computer readable material for use by computers, softwareresident in memory or a programmed chip for use in computers ordedicated equipment, or some combination of both hardware and software.The dedicated equipment may be part of a larger device that wouldcomplement the dedicated equipment's purpose.

Given the following enabling description of the drawings, the inventionshould become evident to a person of ordinary skill in the art.

DESCRIPTION OF THE DRAWINGS

FIG. 1(a) is a conceptual diagram representation of the inventionincluding the fine-tuning alternative embodiment. FIG. 1(b) graphicallyshows the combination of output from the functions represented by FIG.3(a) with time of day modulation to derive predicted cognitiveperformance.

FIG. 2 is a block diagram representation of the wake, sleep, delay, andsleep inertia functions for calculating predicted cognitive performancecapacity.

FIG. 3(a) graphically illustrates the effect of being awake and asleepon cognitive performance capacity over a 24-hour period. FIG. 3(b) is anenlarged view of circled portion 3(b) of FIG. 3(a), and graphicallyshows the delay function with respect to cognitive performance capacity.FIG. 3(c) is an enlarged view of circled portion 3(c) of FIG. 3(a), andgraphically shows the sleep inertia function with respect to cognitiveperformance capacity.

FIGS. 4(a)-(b) depict a detailed flowchart showing the steps of themethod of the invention.

FIG. 5 illustrates time on task effects across a 10-minute PsychomotorVigilance Task (PVT) sessions at two hour increments during 40 hours oftotal sleep deprivation.

FIG. 6 depicts a functional representation of an alternative embodiment.

FIG. 7(a) illustrates a block diagram of structural components for thepreferred embodiment. FIG. 7(b) illustrates a block diagram of analternative set of structural components.

FIGS. 8(a)-(b) depict a detailed flowchart showing the steps of analternative embodiment.

DETAILED DESCRIPTION OF THE INVENTION

The present invention now is described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Thepresent invention will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. Like numbers refer to like elementsthroughout.

As will be appreciated by one of skill in the art, the present inventionmay be embodied as a method, data processing system, or computer programproduct. Accordingly, the present invention may take the form of anentirely hardware embodiment, an entirely software embodiment or anembodiment combining software and hardware aspects. Furthermore, thepresent invention may take the form of a computer program product on acomputer-usable storage medium having computerusable program code meansembodied in the medium. Any suitable computer readable medium may beutilized including hard disks, CD-ROMs, optical storage devices, ormagnetic storage devices.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java®, Smalltalk or C++. However, the computer program code forcarrying out operations of the present invention may also be written inconventional procedural programming languages, such as the “C”programming language.

The program code may execute entirely on the user's computer, as astandalone software package; on a remote computer; or it may executepartly on the user's computer and partly on a remote computer. In thelatter scenario, the remote computer may be connected directly to theuser's computer through a LAN or a WAN (Intranet), or the connection maybe made indirectly through an external computer (for example, throughthe Internet using an Internet Service Provider). The invention may beimplemented as software that may be resident on a stand-alone devicesuch as a personal computer, a PAL device, a personal digital assistant(PDA), an e-book or other handheld or wearable computing devices(incorporating Palm OS, Windows CE, EPOC, or future generations likecode-named products Razor from 3Com or Bluetooth from a consortiumincluding IBM and Intel), or a specific purpose device having anapplication specific integrated circuit (ASIC).

The present invention is described below with reference to flowchartillustrations of methods, apparatus (systems) and computer programproducts according to an embodiment of the invention. It will beunderstood that each block of the flowchart illustrations, andcombinations of blocks in the flowchart illustrations, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the flowchart block or blocks.Examples of how the software can be stored for use are the following: inrandom access memory (RAM); in read only memory (ROM); on a storagedevice like a hard drive, disk, compact disc, punch card, tape or othercomputer readable material; in virtual memory on a network, a computer,an intranet, the Internet, the Abilene Project, or otherwise; on anoptical storage device; on a magnetic storage device; and/or on anEPROM.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

The present invention involves a method for predicting cognitiveperformance at a given time in the past, present, or future as aconsequence of the amount of sleep and wakefulness up to that time as afunction of the time of day and the workload for a particularindividual. The method calculates a numerical estimate of cognitiveperformance for an individual as a continuous function of time. Thecalculations (described below) are based on empirically derived directmathematical relationships among (1) the continuous decrement ofcognitive performance during wakefulness; (2) restoration of cognitiveperformance during sleep; (3) cyclic variation in cognitive performanceduring the course of the day; and (4) variations in cognitiveperformance due to whether and what activities are occurring.

In accordance with the invention, a numeric value indicating predictedcognitive performance at a given moment in time is provided as shown inFIGS. 1(a)-(b). As shown in FIG. 1(a), predicted cognitive performanceequals the output of a series of calculations and/or determinationsobtained in three general steps, using functions empirically derivedfrom direct measurements of cognitive performance under scientificallycontrolled conditions. The first step, as shown in FIG. 2, preferablyuses a set of functions to calculate an initial value referred to as thelevel of cognitive performance capacity as graphically depicted in FIGS.3(a)-(c). Once the level of cognitive performance capacity iscalculated, the second step preferably calculates or uses a previouslycalculated time of day modulator M represented as G8 in FIG. 1(b) and S8in FIG. 4(b). The third step preferably calculates a task modulator Trepresented as S9-S10(b) in FIG. 4(b). Alternatively, the second andthird steps may be switched and/or combined. The fourth step preferablyinvolves the mathematical combination of the results from the firstthrough third steps yielding a predicted cognitive performance, shown asa block diagram in FIG. 1(a) and graphically represented in FIG. 1(b),which illustrates the combination of the cognitive performance capacityand the time of day modulator.

There are four functions relating to the sleep/wake history used tocalculate the level of cognitive performance capacity as shown in FIGS.2-4(b). The wake function w(t) quantifies empirically derivedrelationships between the time awake and degradation of cognitiveperformance. The sleep function s(t) quantifies empirically derivedrelationships between the time asleep and maintenance and/orrecuperation of cognitive performance. In addition to these two primaryfunctions that operate during the bulk of the time awake or asleep thereare two other functions that operate briefly during the transition fromone state to the other. They include the delay of recuperation functiond(t) and the sleep inertia function i(t). The delay of recuperationfunction d(t) represents the relationship between the wake to sleeptransition and the recuperation of cognitive performance. This functionoperates during the initial period of sleep following being awake, knownas stage 1 sleep, as shown in FIG. 3(b). The sleep inertia function i(t)represents the relationship between the sleep to wake transition andcognitive performance. This function operates during the initial periodof time being awake after being asleep as shown in FIG. 3(c).

The function representing the time of day effects on cognitiveperformance is used to calculate a modulating factor M. The time of dayfunction describes empirically derived relationships between the time ofday (point in time within a 24-hour period) and the variation incognitive performance over the course of the day as exemplified by G8 inFIG. 1(b).

The function representing the task/activity impact on cognitiveperformance is used to calculate a modulating factor T. The taskfunction describes the impact of the performance of a task and/or anactivity upon cognitive performance preferably based upon, for example,the intensity, the length, the complexity, and the difficulty associatedwith the particular task and/or activity. FIG. 5 illustrates the impactof performing a task across a 10-minute Psychomotor Vigilance Task (PVT)session at two hour increments during 40 hours of total sleepdeprivation. For each PVT session, except the last one, there was animprovement from trial 10 of one PVT session to trial 1 of the next PVTsession.

A mathematical operation, shown in FIG. 1(b) as multiplication, is usedto combine the results from the first, second, and third steps into asingle predicted cognitive performance curve E in the fourth step.

Using the preferred embodiments, predicted cognitive performance E cantheoretically reach an index level of 120, but only when cognitiveperformance capacity C is an index level of 100 (i.e., 20 minutes afterawakening from a sleep period in which cognitive performance capacity Cwas fully restored) and simultaneously the time of day function M is atits acrophase. Although possible, in practice this situation isunlikely.

The inputted data S2 into the method includes a representation of anindividual's sleep/wake history and task information. The sleep/wakehistory is a time series or temporal record based on local clock time.Each successive period, interval or epoch identifies one of two mutuallyexclusive states, namely wake or sleep. The task information is a seriesof information regarding what the individual is or is not performing inthe way of activities/tasks and alternatively the intensity, thedifficulty, the length and/or the complexity of the activity/task may beincluded in the task information. Both the sleep/wake history and thetask information are not necessarily “historical” in the sense ofoccurring in the past, but may for example be hypothetical, projected,idealized, or contemplated. The latter in particular are appropriate forthe predictive uses of this method.

The gold standard for measuring sleep and wakefulness is polysomnography(PSG). PSG sleep scoring is based on the concurrent recording, or atleast recording in such a way as allows the latter synchronization(typically with time-stamping or time-linking) of the data, ofelectroencephalogram (EEG), electrooculogram (EOG), and electromyogram(EMG). These signals are typically then visually inspected on anepoch-by-epoch basis (each epoch traditionally is 30 seconds in lengthfor PSG) to determine an individual's stage of sleep or wakefulness.Polysomnographic sleep scoring distinguishes between wake, non-rapid eyemovement sleep (NREM) and rapid eye movement sleep (REM), with NREMsleep being further distinguished into four stages (stages 1, 2, 3, and4) on the basis of characteristic EEG markers. PSG is not a practicalmethod for determining sleep and wakefulness in applied settings (e.g.,while driving, working, or on the battlefield), because PSG requiresthat individuals be attached to sensors or electrodes that connect witha recording device, and currently the only accepted method for scoringPSG is by visual inspection of the recorded EEG, EOG, and EMG results.

Presently, if a computer is used for scoring PSG, then typically a humanreviews the results for accuracy in the scoring, because computerscoring has not been approved by the American Sleep DisordersAssociation. Also, recently, researchers have been exploring whetherspectrally analyzed PSG or similar data using Fast Fourier Transformsmight provide a better measurement of sleep in humans than PSG scoring.

A preferred method of determining sleep from wakefulness would be adevice that is portable, unobtrusive, reliable, and whose recordings canbe scored automatically. One such method is monitoring of movementactivity, or actigraphy. The movement activity device is typically wornon the non-preferred wrist, but may be placed elsewhere on an individual(e.g., the ankle). When worn on the non-preferred wrist, these deviceshave been shown to accurately quantify sleep and wakefulness as comparedto the standard provided by PSG (reliabilities as high as 90%).

The most widely used method of scoring actigraphy data is an algorithmdeveloped by Cole and associates and described in their article entitled“Automatic Sleep/Wake Identification from Wrist Actigraphy” published inSleep, vol. 15, pp. 461-469 (1992). Successful actigraphy sleep-scoringalgorithms such as the Cole et al. algorithm (also known as theCole-Kripke algorithm) are for use with conventional(number-of-zero-crossings) actigraphs, and some algorithms account forthe number of counts above a certain threshold. These algorithms arelimited to making simple sleep vs. wake distinctions, and cannotdistinguish sleep stage changes (e.g., Stage 1 to Stage 2, or Stage 2 toREM) within sleep itself. Consequently, such algorithms cannotdiscriminate recuperative sleep (stages 2, 3, 4, and REM) fromnon-recuperative sleep (stage 1).

More recently, digital signal processing (DSP) actigraphs have begun tobe developed. Because the DSP actigraph will provide much moreinformation than just the conventional number of zero crossings orcounts above threshold (this and other information provided by aconventional actigraph will, however, be retained), it shows promise fordistinguishing between different sleep stages. Thus, sleep scoringsystems for DSP will not only replace, but will also make irrelevant,the Cole-Kripke algorithm. A sleep scoring system for the DSP will bedeveloped as the DSP database of empirical data from use of DSPactigraphs increases.

Other algorithms and methodologies for automated actigraphy scoring havebeen developed by, for example, Jean-Louis et al., 1996; Sadeh et al.,1989; and Zisapel et al., 1995. Each of these scoring systems showsconsiderable promise, especially for scoring the actigraphicallyrecorded sleep/wake states of individuals with sleep disorders or othermedical disorders. Available scoring systems mainly differ alongtechnical aspects, for example, the extent to which activity counts inprevious and subsequent epochs influence the scoring of the currentepoch; and variation among mathematical principles underlying eachscoring system. As one of ordinary skill in the art will realize, anyactigraph scoring system is capable of providing the sleep/wake datainput for the method of this invention.

The sleep/wake history will preferably take the form of a data series.The sleep/wake history may include past, present, and/or future(predicted) sleep/wake patterns of an individual. The sleep/wake historyis a representation of a state of an individual as either being asleepor awake and is divided into epochs. The epochs are the same length, butthat length could be of any time period as dictated by restraints of themethod and apparatus used to collect data and/or the desired precisionof the sleep/wake pattern. The PSG or similar scoring can be convertedinto a sleep/wake history for an individual.

It can be appreciated that the accuracy of the cognitive performanceprediction is directly related to the accuracy of the sleep/wake historyinput and the sleep scoring system used to interpret the sleep/wakestates of an individual. One possible source of inaccuracy may arisefrom the temporal resolution of the input epoch or interval. That is,the shorter the input epoch, the higher the temporal resolution andconsequent moment-to-moment accuracy of the sleep/wake input. Forexample with actigraphy, past experience indicates that the mosteffective length of an epoch is one minute. Another source of inaccuracymay arise from ambiguity in the sleep/wake discrimination itself. In theevent that the history input is ambiguous (i.e., the sleep or wake stateis uncertain), the calculation of predicted cognitive performance can beperformed twice concurrently, once for each possible state (sleep orwake), resulting in a dual output representing the possible range ofexpected cognitive performance. One of ordinary skill in the art willappreciate that the dual output can be further divided if there is morethan one ambiguity in the sleep/wake history. Such treatment inexecuting the functions expressed below is included as a component ofthis method and any implementing apparatus.

The method of this invention is not limited with regard to time ortechnique: on-line/real-time vs. off-line/post-hoc; or incremental,iterative vs. discrete solutions to continuous forms of those equations.

A preferred embodiment of the method encompasses a mathematical modelthat expresses predicted cognitive performance capacity E at time t as amodulation of the current cognitive performance capacity C by a time ofday function M by a task function T. It can be written as a generaldescription in its simplest form as:

E=C∇M∇T  Equation 1

where ∇ represents a mathematical operator. Any mathematical operatormay be used to combine cognitive performance capacity C, day of timefunction M, and task function T. The form and nature of time of dayfunction M and/or task function T dictate the exact operator that ismost desirable. There may be two different operators used to express thepredicted cognitive performance capacity E such that the first ∇ may beone mathematical operator and the second ∇ may be a second mathematicaloperator. Alternatively, the modulations could be performed in two stepswhere two of the items are modulated with the resulting modulated valuebeing modulated with the third of the items. Most preferably, Equation1a below would be used to combine cognitive performance capacity C, dayof time function M, and task function T.

E=C*M*T  Equation 1a

In the alternative, Equation 1b below could also be used to combinecognitive performance capacity C, time of day function M, and taskfunction T.

E=C+M+T  Equation 1b

Cognitive performance capacity C represents a function of sleep/wakehistory, that is

C=w(t)+s(t)+d(t)+i(t)  Equation 2

where w(t), s(t), d(t), and i(t) are the instantaneous values of thewake, sleep, delay, and sleep inertia functions at time t. Time of dayfunction M represents a function of the time of day, such that

M=m(t)  Equation 3

where m(t) is the instantaneous value of the time of day function attime t. Task function T represents a function of the impact ofperforming or not performing a task when the individual is awake, suchthat

T=t(t)  Equation 4

In keeping with the invention, a four-step process may be performedafter either an initial setting of the starting time t, the startingcognitive performance capacity C, and the time of the last transitiont_(LS) when appropriate in S1 of FIG. 4(a) where these data can beentered in any order. In the first step, the level of cognitiveperformance capacity C at time t may be calculated based on anindividual's sleep/wake history using functions w(t), s(t), d(t), andi(t) as represented by S3-S7 e in FIGS. 4(a)-(b). In the second step,time of day modulator M may be calculated using the time of day functionas represented by S8 in FIG. 4(b). According to an aspect of theinvention, the second step can be performed once to provide a series ofdata points in time sequential order for multiple executions of thefirst step. In the third step, task modulator T may be calculated usingthe task function as represented by S9 a through S10 c in FIG. 4(b). Inthe fourth step, predicted cognitive performance E may be derived fromthe combination of cognitive performance capacity C and time of daymodulator M resulting in cognitive performance capacity C beingmodulated by time of day modulator M being modulated by task modulator Tas illustrated by S11 in FIG. 4(b).

First Step: Calculation (or Determination) of Cognitive PerformanceCapacity C

FIG. 2 is a schematic flow diagram representing the use of the functionsdescribed below. Examples of the calculations discussed are graphicallyillustrated in FIGS. 3(a)-(c). FIGS. 4(a)-(b) are a detailed flowchartof the steps in the method. As a preferred embodiment of the model,cognitive performance capacity C is herein assigned index valuespreferably having a total range of zero to 120. The ranges in thisapplication are intended to encompass the end points of the statednumerical range. However, cognitive performance capacity C may be scaledto other values or units for specific applications, for example, zero to100.

In the preferred embodiment, only one of the four functions w(t), s(t),d(t), and i(t) operates at any given interval of time, and the othersare equivalent to zero in Equation 2 as represented by S7 a through S7d. Functions w(t) and s(t) describe the non-transition states, whilefunctions d(t) and i(t) describe the transition states. For instance ina non-transition state, when the individual is awake, function s(t) isset to zero, and when the individual is asleep, function w(t) is set tozero. Likewise, during specific intervals of transition from wake tosleep and vice versa, only one of the transition functions d(t) or i(t)operates, the other being set equal to zero. When there is a changebetween sleep and wake, or vice versa, a time counter t_(LS) is reset tokeep track of the time in the present state for determining decisionrules for the transition functions d(t) and i(t) as shown in FIG. 4(b).

(1) Wake Function (w(t))

The wake function S7 a represents the depletion of cognitive performancecapacity with the passage of time awake. It is based on evidence that(1) near-100% cognitive performance is maintained from day to day whenindividuals obtain eight hours of sleep each night; and (2) cognitiveperformance appears to decline by approximately 25% for every 24 hoursof wakefulness.

In S7 a, the wake function w(t) calculates the current value ofcognitive performance capacity C resulting from the decay in cognitiveperformance capacity that occurs over an interval of time from t−1 to t,which in the preferred embodiment is the length of one epoch. As notedabove, this calculation is performed independent of and prior tomodulation of cognitive performance capacity C by the time of dayfunction M in S9. A generalized form of the wake function is given bythe equation:

C _(w) =w(t)  Equation 5

where wake function w(t) may be any positive-valued function decreasingwith t. More preferably, the wake function w(t) is a linear functiondepleting performance at a constant rate, and, most preferably, the wakefunction w(t) is expressed at time t as follows:

w(t)=C _(t−1) −k _(w)  Equation 5a

where the interval of wakefulness is from t−1 to t (in epochs) and thedecay in performance per minute is k_(w). Thus, if t−1 to t is not oneminute, then k_(w) is appropriately adjusted. The total range of k_(w)is any positive real number, and preferably k_(w) is a range of 0.003 to0.03 of a point per minute, and most preferably k_(w) is equal toapproximately 1 point per hour or 0.017 of a point per minute. The valuek_(w) is based on empirical data showing that cognitive performancedeclines by approximately 25 points for every 24 hours of continuouswakefulness. Equation 4a is represented in FIGS. 2 and 4(b) at S7 a. Anexample is illustrated as the wake function in FIG. 3(a), for an initialcognitive performance capacity of 100 index points, a decay rate of0.017 of a point per minute, over an interval of 16 hours (960 minutes).

(2) Sleep Function (s(t))

The sleep function S7 c restores cognitive performance capacity with thepassage of time asleep. The sleep function s(t) is based on empiricalevidence that the recuperative value of sleep on cognitive performanceaccumulates in a nonlinear manner. That is, the rate of cognitiveperformance capacity recuperation is higher initially during sleep andslows as the time asleep accumulates. Other data indicates that sleepbeyond a certain point confers little or no additional benefit forcognitive performance and the rate of recuperation approaches zero.Thus, for example, two hours of sleep are not twice as recuperative asone hour of sleep. The sleep function increases cognitive performancecapacity at a rate that depends on the current level of cognitiveperformance capacity—the lower the initial cognitive performancecapacity, the more rapidly recuperation accumulates. In other words,preferably the slope of a tangential line for a particular cognitiveperformance capacity index level is the same each time that index levelis reached during different sleep periods.

For example, following a full day (16 hours) of wakefulness, duringensuing nighttime sleep recuperation accumulates rapidly early in thenight. As cognitive performance capacity is restored across the sleepperiod, the rate of recuperation declines. Following sleep deprivation,initial cognitive performance capacity is even lower than it would befollowing a normal 16-hour day, and the rate of recuperation is evenhigher than at the beginning of recovery sleep. During chronic partialsleep deprivation, cognitive performance capacity may not be completelyrestored each night despite this more rapid initial recuperation rate.

The sleep function calculates the current value of cognitive performancecapacity C resulting from the recovery of capacity that occurs while anindividual is asleep over an interval of time T (from t−1 to t). Asnoted above, this calculation is performed independent of, and prior to,modulation of C by the time of day function M and modulation by the taskfunction T. A generalized form of the sleep function is given by theequation:

C _(s) =s(t)  Equation 6

where sleep function s(t) may be any positive-valued function increasingwith t, and more preferably the sleep function s(t) is an exponentialfunction. This is based on empirical data showing that cognitiveperformance restoration during sleep is nonlinear, with the rate ofrecuperation highest initially and gradually slowing as sleep continues.Thus, the most preferred sleep function is an exponential function,which in its discrete form is stated as:

C _(t) =C _(t−1)+(100−C _(t−1))/k _(s)  Equation 6a

where the interval of sleep is from t−1 to t (in minutes), the maximumcognitive performance capacity value is 100 index points, C_(t−1) iscognitive performance capacity in the period preceding time t, and k_(s)is the recuperation “time constant”. In other words, k_(s) is the timerequired to fully restore cognitive performance capacity C if it wasrestored at a constant rate equal to the initial slope of the curve. Therecuperation time constant k_(s) is derived empirically from partialsleep deprivation data and is selected based on the length of the epoch.In accordance with the preferred embodiment, k_(s) is equal to anypositive real number. For example, k_(s) may be in the range of 100 to1000 with an epoch length of one minute, and, more particularly may beapproximately 300 with an epoch length of one minute. However, theoptimum values for k_(s) will depend at least in part on the length ofthe epoch. Equation 6a is represented in FIGS. 2 and 4(b) as S7 c. Agraphical example is illustrated as the sleep function in FIG. 3(a),using an initial cognitive performance capacity level of 100 indexpoints, and using a time period of one minute and k_(s)=300, the effectof eight hours of sleep following 16 hours of wakefulness.

(3) Delay Function d(t) for Wake to Sleep Transitions

The delay of recuperation function d(t) defines the duration of aninterval after sleep onset during which recuperation of cognitiveperformance capacity from the sleep function is delayed. During thisinterval, the wake function degradation of cognitive performancecapacity continues as represented by S7 d in FIG. 4(b). By preventingimmediate accumulation of cognitive performance capacity at thebeginning of a sleep period or following awakenings from sleep, thisdelay adjusts the cognitive performance capacity calculation S6 b.

The delay of recuperation function is based upon empirical studiesshowing that the first few minutes of sleep are generally comprised ofstage 1 sleep, which is not recuperative for sustaining cognitiveperformance capacity. Frequent arousals to wake or stage 1 sleep (sleepfragmentation) drastically reduce the recuperative value of sleep oncognitive performance capacity. Available data suggest that five minutesis the approximate length of time required to return to recuperativesleep (stage 2 or deeper sleep) following an arousal to wake or stage 1sleep. If many hours of sleep are obtained without interruption, thenthe delays make only a small difference in overall restoration ofcognitive performance capacity. If sleep is interrupted with frequentawakenings, the delays in recuperation after each awakening willaccumulate, and thus substantially reduce total cognitive performancecapacity restored during the total sleep period.

The delay function specifies the duration of a sleep interval duringwhich application of the sleep function is postponed and a transitionalformula is applied. A generalized form of the delay function for wake tosleep transitions is expressed as a decision rule:

d(t): IF (t−t _(LS))≦k _(d)

THEN C _(t) =d(t)

ELSE C _(t) =s(t)  Equation 7

where LS stands for last state change, and thus the wake to sleeptransition time t_(LS) denotes the time of the last wake intervalpreceding a contiguous series of sleep intervals. This decision rule isshown in FIGS. 2 and 4(b) as S6 b, S7 c and S7 d taken together. Forcalculating cognitive performance capacity during the interval k_(d),cognitive performance capacity C_(t) is evaluated by a transitionalformula C_(t)=d(t). After k_(d) has elapsed, C_(t)=s(t). Note that ifwakefulness ensues before the end of k_(d), then C_(t) never reverts tos(t). That is the sleep function is not applied during the brief sleepinterval.

It is believed that the preferred range for k_(d) is from 0 to 30minutes, more preferably k_(d) equals about five minutes measured fromthe time of sleep onset before recuperation is derived from sleep.Preferably d(t) equals w(t). One of ordinary skill in the art willrealize there are a variety of factors that influence the length ofk_(d). Thus a more preferred delay function may be expressed as:

d(t): IF (t−t _(LS))≦5

THEN C_(t) =w(t)

ELSE C_(t) =s(t)  Equation 7a

The effects of delayed recovery on cognitive performance capacity, asembodied by Equation 7a, are graphically illustrated in detail in FIG.3(b).

As one of ordinary skill in the art will appreciate, PSG or similarscoring is able to classify when stage 1 sleep occurs. The conversion ofPSG or similar scoring data would then convert the occurrences of stage1 sleep into wake data for the sleep/wake history. Consequently, whenthe sleep/wake history is based on converted PSG or similar scoringdata, the delay function d(t) is not necessary for the determination ofan individual's cognitive performance capacity. Alternatively, the delayfunction could be determined based upon when the individual enteredstage 2 or deeper sleep instead of using the k_(d) value, and that oncestage 2 or deeper sleep is reached then the sleep function s(t) would beused.

Alternatively, the delay function d(t) may simply maintain the cognitivelevel of C_(t) at the beginning of the delay period, i.e., CtLS.

(4) Sleep Inertia Function i(t) for Sleep to Wake Transitions

The sleep inertia function i(t) defines the duration of an intervalafter awakening from sleep during which manifest cognitive performancecapacity is suppressed below the actual current level. The sleep inertiafunction i(t) is based upon empirical data showing that cognitiveperformance is impaired immediately upon awakening, but improvesprimarily as a function of time awake. It is also based on positronemission tomography studies showing deactivated heteromodal associationcortices (those areas that mediate this cognitive performance)immediately upon awakening from sleep, followed by reactivation of theseareas over the ensuing minutes of wakefulness. That is, actual cognitiveperformance recuperation realized during sleep is not apparentimmediately after awakening. The data indicate that 20 minutes is theapproximate length of time required for cognitive performance capacityto return to levels that reflect actual recuperation accrued duringsleep.

A sleep inertia delay value k_(i) specifies the duration of the intervalafter awakening during which manifest cognitive performance capacity maybe transitionally suppressed below the sleep-restored cognitiveperformance capacity level. During this interval, a transitionalfunction bridges from an initial level to that determined by the wakefunction alone. A generalized form of the sleep inertia function forsleep to wake transitions is expressed as a decision rule:

i(t): IF (t−t _(LS))<k _(i)

THEN C _(t) =i(t)

ELSE C _(t) =w(t)  Equation 8

where the sleep to wake transition time t_(LS) denotes the time of thelast sleep interval preceding a contiguous series of wake intervals. Forcalculating cognitive performance capacity during the interval k_(i),C_(t) is evaluated by a transitional formula C_(t)=i(t). After k_(i) haselapsed, C_(t)=w(t). Equation 8 is represented in FIGS. 2 and 4(b) as S6a, S7 a and S7 b taken together.

The preferred range for k_(i) is from 0 to about 60 minutes, andpreferably in the range of about 10 to about 25 minutes, and mostpreferably between 18 and 22 minutes.

The sleep inertia function i(t) may be any function over the interval 0to k_(i), preferably any negatively accelerated function. A preferredsleep inertia function i(t) is a simple quadratic equation. Thisfunction preferably suppresses cognitive performance capacity by 10% to25% immediately upon awakening, and most preferably by 25%. The functionrecovers 75% of the suppressed cognitive performance capacity in thefirst 10 (or about half of k_(i)) minutes after awakening and 100% ofthe suppressed cognitive performance capacity usually by 20 minutesafter awakening, after which the wake function resumes. These values arebased on empirical data concerning the transition from sleep to wake.These studies show that cognitive performance is impaired immediatelyupon awakening from sleep, that the bulk of this impairment dissipateswithin the first few minutes of awakening, and that approximately 20minutes is required for performance to be fully restored. Using thepreferred 25% suppression of cognitive performance capacity and 20minute recovery time, the preferred form of the sleep inertia functionis expressed as a decision rule:

i(t): IF (t−t _(LS))<20

THEN C _(t) =C _(sw)* [0.75+0.025 (t−t _(LS))−(0.025 (t−t _(LS)))²]

ELSE C _(t) =w(t)  Equation 8a

where C_(sw) is cognitive performance capacity at the end of the sleepperiod calculated by the sleep function at the sleep to wake transitiontime t_(LS). This decision rule is shown in FIGS. 2 and 4(b) as S6 a, S7a, and S7 b taken together. Equation 8a illustrates an initialsuppression of 25% and k_(i) equal to 20 minutes, and a negativelyaccelerated ramp bridging the interval until the wake function w(t)resumes its effects. The effect of the sleep inertia function i(t) oncognitive performance capacity, as embodied by Equation 8a, isgraphically illustrated in FIG. 3(c). An alternative variant of thesleep inertia function i(t) is a linear equation based on k_(i) equal to10 minutes and an initial 10% decrease in cognitive performancecapacity. The resulting decision rule is then:

 i(t): IF (t−t _(LS))<10

THEN C _(t) =C _(sw)* [0.9+(t−t _(LS))/100]

ELSE C _(t) =w(t)  Equation 8b

As one of ordinary skill in the art will realize, both Equations 8a and8b can be adjusted for a change in the value of k_(i) and amount of theinitial suppression of cognitive performance capacity.

Second Step: Calculation of the Time of Day Modifier M

(1) Time of day function m(t)

The time of day function m(t) shown at S8 in FIG. 4(b) describes thecyclical 24-hour variation in cognitive performance. The time of dayfunction m(t) is based on empirical data showing that under constantroutine and/or total sleep deprivation conditions (i.e., with sleep/wakehistory controlled), cognitive performance capability oscillates betweenapproximately 5% to approximately 20% peak to peak over a 24-hourperiod. This effect is commonly attributed to circadian rhythms ofindividuals. Output from this function modulates the current cognitiveperformance capacity prediction C (calculated in the first step)according to the time of day. The result of this modulation is thepredicted cognitive performance capacity E. A generalized form of thetime of day function is given by

M=m(t)  Equation 9

where m(t) can be any rhythmic function with a base period of 24 hours,and, preferably, m(t) is the sum of two sinusoids, one with a period of24 hours and the second with a period of 12 hours, which provides abiphasic circadian component. This function may be based on empiricaldata showing that a considerable proportion of variability seen incognitive performance measurements can be accounted for by two suchsinusoidal waveforms. As previously noted, the peak in empiricallyobserved cognitive performance capacity occurs usually between 8:00 PMand 10:00 PM, and the trough occurs usually between 2:00 AM and 6:00 AM,providing a variation of approximately 5% to approximately 20% each day.A secondary trough occurs usually around 3:00 PM. Using these values forthe preferred form of function m(t), the resulting function accounts forthe empirically demonstrated asymmetry of daily cognitive performancerhythms, with a mid-afternoon decrease.

The descriptive form of the function m(t), including its offset andamplitude values varies with the operator selected for the third step.The computed value of the function can be expressed either as anadditive percentage of cognitive capacity (dependent or independent ofthe current value of cognitive performance capacity C_(t)) or as amultiplicative dimensionless scalar. The preferred form of the function,using the multiplicative operator, is expressed as

m(t)=F+(A ₁ * cos (2Π(t−V ₁)/P ₁)+A ₂ * cos (2Π(t−V ₂)/P ₂))  Equaton 9a

where F is an offset, t is the time of day, P₁ and P₂ are periods of twosinusoids, V₁ and V₂ are the peak times of day in time units or epochspast midnight, and A₁ and A₂ are amplitudes of their respective cosinecurves. This function may be used to modulate the previously calculatedcognitive performance capacity C. Equation 9a is shown as S8 in FIGS.1(a) and 4(b) and graphically illustrated as G8 in FIG. 1(b). As shownin FIG. 4(b), t is an input in the time of day function m(t) for eachepoch of data.

For example in a preferred embodiment the variables are set as follows:t is the number of minutes past midnight, P₁ is equal to 1440 minutes,P₂ is equal to 720 minutes, V₁ is equal to 1225, and V₂ is equal to 560.Further, when A₁ and A₂ are represented as scalars, their amplitudes arein a range from 0 to 1, and more preferably are in a range from 0.01 to0.2, and most preferably A₁ is equal to 0.082 and A₂ is equal to 0.036.Further in this example F is equal to either 0 or 1, and more preferablyF is equal to 1. The resulting value of the time of day function m(t),in this example, is in the range of 0 to 2, and preferably in the rangeof 0.8 to 1.2, and most preferably in the range of 0.92 to 1.12.

As mentioned above, the second step may, for example, be preformed onthe fly, for example, in real time or be previously calculated prior tothe first step.

Third Step: Calculation of the Time on Task Modulator T

In the preferred embodiment, only one of the two functions g(t) and h(t)operates during any period in which the individual is awake with theother function being equivalent to zero. However, when the individual isasleep then both functions g(t) and h(t) are equal to zero asrepresented by S9 a through S10 c in FIG. 4(b). The selection of thefunction preferably is based upon whether the individual is performing atask or not is illustrated by S9 b through S10 b and the individual isawake is illustrated by S9 a and S10 c. As such, the time on taskmodulator may be calculated prior to (as illustrated in FIG. 8(a)) orafter steps S7 a and S7 b instead as a separate branch as illustrated inFIG. 4(b).

(1) Rest Function g(t)

The rest function g(t) is illustrated as S10 a in FIG. 4(b). The restfunction g(t) preferably represents the restoration of cognitiveperformance capacity due to an individual resting and relaxing betweentasks and/or activities. The rest function g(t) preferably does notprovide the same amount of restoration that occurs during sleep asdiscussed above with respect to the sleep function s(t). A generalizedform of the rest function is given by the equation:

t(t)=g(t)  Equation 10

where g(t) may be any positive-valued function. Alternatively, the restfunction g(t) may be expressed as follows:

g(t)=z * s(t)  Equation 10a

where z is a scalar, which preferably is in a range of 0 to 1, andt_(LS) will preferably represent the length of the resting and/orinactivity period.

(2) Work Function h(t)

The work function h(t) is illustrated as S10 b in FIG. 4(b). The workfunction h(t) preferably represents declination of cognitive performancecapacity due to an individual performing a task(s) and/or anactivity(ies). In S10 b, the work function h(t) calculates the taskmodulator T resulting from the decay in cognitive performance capacitythat occurs over an interval of time from t−1 to t, which in thepreferred embodiment is the length of one epoch. A generalized form ofthe work function is given by the equation:

t(t)=h(t)  Equation 11

where h(t) may be any negative-valued function decreasing with t. Morepreferably, the work function h(t) is a linear function depletingperformance at a constant rate. Alternatively, the work function h(t)may be an exponential function that, for example, may increase thedepletion rate the longer the task is performed and/or activity is done.Another or further alternative is that the type of task, i.e., thedifficulty, the complexity, and/or the intensity will impact thedepletion rate per epoch. The greater the difficulty, the complexity,and/or the intensity of the task, then the greater the depletion rateper epoch will be.

Alternatively, both or just one of the rest function and the workfunction may be impacted by the time of day modulator M such that priorto being modulated with the cognitive performance capacity C and thetime of day modulator M, the task modulator T is modulated based uponthe time of day as represented by the time of day modulator M. A furtheralternative is for the time of day modulator M to be used twice inEquations 1, 1a, and 1b above.

(3) Asleep Function

A generalized form of the task function when the individual is asleep is

t(t)=1  Equation 12

where the modulation is performed using multiplication, because the taskfunction T will not impact the individual's cognitive performance index.Alternatively, if the task modulator is added to the other functions,then the task function will take the following form

t(t)=0  Equation 12a

Fourth Step:Calculation of Predicted Cognitive Performance

The overall process of calculating predicted cognitive performancecapacity E is illustrated schematically in FIGS. 1(a) and 4(a)-(b). Thetime of day function M and the task function T modulate the cognitiveperformance capacity C derived from the individual's sleep/wake historyto generate the final predicted cognitive performance E as shown in, forexample, FIG. 1(a). In the third step, predicted cognitive performance Eis derived from the combination of cognitive performance capacity C,time of day function M, and task function T. In its most general form:

E=C∇M∇T  Equation 1

where ∇ is any mathematical operation for combining cognitiveperformance capacity C, time of day function M, and task function T. Theconventional choice of operations for providing this combination isaddition or multiplication. Depending on the form of time of dayfunction m(t) and task function T(t) selected above, the same numericalvalue of predicted cognitive performance E can be generated by eitheroperation. Most preferably the combination is performed withmultiplication S11, represented as:

 E=C*M*T  Equation 1a

In Equation 1a, the predicted cognitive performance E is the modulationof the current cognitive performance capacity C with a value centeredaround the number one representing the current value of the time of daymodulator M and the task modulator T.

As noted above, the preferred numerical representation of cognitiveperformance capacity C is a value ranging from zero to 100 to representan index (or a percentage) of cognitive performance capacity availablefor a particular individual. However, predicted cognitive capacity E canmeaningfully exceed 100 under certain circumstances due to time of daymodulation about the current value of cognitive performance capacity C.A possible example of such a circumstance would be a sleep periodresulting in an index level of 100 cognitive performance capacity C andterminated at the evening peak (and after sleep inertia has dissipated).

Alternatively, if a percentage representation is used while retaining a100% scale, either the predicted cognitive capacity E may betruncated/clipped at 100% or 0 to 120% may be scaled to 0 to 100%.Either choice will maintain a maximum of 100%. This most likely will beimplemented as scaling 120% to 100% and then truncating/clipping anypredicted cognitive capacity E to 100% if the prescaled value exceeds120%.

As shown in FIG. 1, the method repeats for each new epoch of data. Foreach iteration of the method, one time unit equal to the length of anepoch may be added to time t preferably in the form of a counter S13 asexemplified in FIG. 4(b). The counter step S13 may occur, for example,as illustrated in FIG. 4(b), at the same time as S11 or S12, or afterS12.

In the preferred embodiment described above, the sleep inertia functioni(t) is applied to cognitive performance capacity C prior to modulationof cognitive performance capacity C by the time of day modulator Mand/or task modulator T. An alternative embodiment applies the sleepinertia function i(t) not to cognitive performance capacity C, but topredicted cognitive capacity E, that is, subsequent to the modulation ofcognitive performance capacity C by time of day modulator M and/or taskmodulator T.

Also in the preferred embodiment described above, the wake function w(t)is set to zero when the sleep inertia function i(t) is applied. Anotheralternative embodiment applies the sleep inertia function i(t) and thewake function w(t) simultaneously. When the sleep inertia function i(t)and the wake function w(t) become equal to each other or the sleepinertia function i(t) becomes greater than the wake function w(t), thencognitive performance capacity C is calculated (or determined) using thewake function w(t).

The preferred embodiment may be further modified to account for theeffects of narcotics or other influences that will impact the cognitivecapacity as shown in FIG. 6. Further modification to the preferredembodiment will allow for the inclusion of jet lag and similar timeshifting events by, for example, compressing or expanding the 24 hourperiod of the time of day function M(t) over a period of days torealigning the time of day function M(t) to the adjusted schedule.

The preferred embodiment may be modified to include the testing of theindividual at regular intervals to collect additional data and adjustthe current cognitive performance index to reflect the results of thetest. A test that may be used is the PVT or similar reaction timemeasurement test(s). The current cognitive performance index at the timeof the test then is adjusted preferably along with the underlyingweights of variables discussed above in connection to the Equations suchthat the method and/or apparatus is fine-tuned to reflect a particularindividual's recuperation and/or depletion of cognitive performancecapacity.

Another alternative embodiment is the removal of the third step from thepreferred embodiment. Like the other alternative embodiments, thisalternative embodiment may be combined in a variety of ways with theother alternative embodiments.

Implementation of the Method

The preferred embodiment may be realized as software to provide arealtime current state of an individual's cognitive performance and thecapability upon demand to extrapolate future levels of cognitiveperformance. A flowchart representing the steps to be performed by thesoftware in the preferred embodiment is shown in FIGS. 4(a)-(b) and foran alternative embodiment, to be described later, in FIGS. 8(a)-(b).

The software may be implemented as a computer program or otherelectronic device control program or an operating system. The softwareis preferably resident in a device, e.g. an actigraph, attached to theindividual or in a stand-alone device such as a personal computer, a PALdevice, a personal digital assistant (PDA), an e-book or other handheldor wearable computing devices (incorporating Palm OS, Windows CE, EPOC,or future generations like code-named products Razor from 3Com orBluetooth from a consortium including IBM and Intel), a specific purposedevice receiving signals from a device, e.g. an actigraph, attached toan individual or human input from human analysis or observation. Thesoftware could be stored, for example, in random access memory (RAM); inread only memory (ROM); on a storage device like a hard drive, disk,compact disc, punch card, tape or other computer readable material; invirtual memory on a network, a computer, an intranet, the Internet, theAbilene Project, or otherwise; on an optical storage device; on amagnetic storage device; and/or on an EPROM. The software may allow forthe variables in the equations discussed above to be adjusted and/orchanged. This capability will allow users to adjust the variables basedon empirical knowledge and also learn the interrelationship between thevariables.

The software implementation onto the measuring device such as anactigraph will convert any decimal numbers used in calculations intointegers that are appropriately scaled as is well known to those skilledin the art. Further the integers would then be approximated such thatminimal error would be created, for example, approximation for theCole-Kripke algorithm weighting factors become 256, 128, 128, 128, 512,128, and 128, respectively. Using linear approximation will simplify thebinary arithmetic and the corresponding assembly code for softwareimplementation.

In software, the time of day modulator would be embodied as a table withone hour steps resulting in 24 rows using 8-bit unsigned integers. Theintervening steps would be interpolated from the one hour steps toprovide 15-minute steps. This simplification provides sufficientresolution for available displays. A pointer system would be utilized toretrieve the appropriate data to calculate the time of day modulator.Depending on a myriad of factors, one of ordinary skill in the art willmost likely choose a multiplicative modulation to achieve appropriatescaling or an additive modulation for less complex but more rapidevaluation, i.e., if speed is a concern. The main disadvantage with theadditive modulation is that there will be an approximately 3% errorcompared to the 1% error using the multiplicative modulation in thisinvention. This system will allow the time of day function to beuploaded when the measuring/recording device, such as an actigraph, isinitialized and reduce the repetitive computational burden that wouldexist if a cosine table was used and the time of day function wascalculated from the cosine table for each epoch.

The preferred embodiment, as shown in FIG. 7(a), may also be realized bya stand-alone device or a component add-on to a recording device. Thestand-alone device is separate from the device or other means ofrecording an individual's sleep history. In contrast, the componentadd-on to a recording device includes modifying the recording device toinclude the component add-on to provide one device that both records andanalyzes an individual's sleep history.

A suitable stand-alone device includes a physical input connection,e.g., an input port (input means 20) to be physically connected to aninput device, e.g., a keyboard, data entry device, or a data gatheringdevice such as an actigraph. Alternatively, the physical connection mayoccur over an information network. Alternatively, the input port may bean interface to interact with a user. Alternatively, the physical inputconnection may be realized by a wireless communication system includingtelemetry, radio wave, infrared, PCS, digital, cellular, light basedsystem, or other similar systems. The wireless communication system hasan advantage in that it eliminates the need for a physical connectionlike cables/wires, plug-ins, etc. which is particularly convenient whenmonitoring a mobile subject. The data gathering or data entry deviceprovides a sleep history that may include past, present and/orpredicted/anticipated sleep patterns of an individual. Input means 20embodies S1 for initial inputting of information and S2 for thecontinual or one-time loading of data depending upon the implementationselected.

The stand-alone device further includes a data analyzer (interpretationmeans 30). The data analyzer performs S3-S6 b. Interpretation means 30analyzes the input data by performing different analysis functions.Interpretation means 30 compares the present input data to the lastinput data to determine if there has been a change from sleep to wake orwake to sleep; and if so, then set a time counter to the time for thelast state, S3 and S4 a in FIG. 4(a). Interpretation means 30 alsoclassifies the inputted data, as represented by S5 in FIG. 4, to then beable to select or generate at least one of the following calculationfunctions responsive to the composition of the input data: 1) wakefunction, 2) sleep function, 3) delay function, and 4) sleep inertiafunction as depicted by S6 a-S7 d in FIG. 4(b). Interpretation means 30may be realized by an appropriately programmed integrated circuit (IC).One of ordinary skill in the art will realize that a variety of devicesmay operate in concert with or be substituted for an IC like a discreteanalog circuit, a hybrid analog/IC or other similar processing elements.

The stand-alone device further includes a calculator (determinationmeans 40). Determination means 40 may be implemented by appropriatelyprogramming the IC of the interpretation means or it may be implementedthrough a separate programmed IC. Determination means 40 calculates thecognitive performance capacity factoring in the sleep/wake history andthe current state using the function selected by interpretation means30, S7 a-S7 d in FIG. 4(b).

The interpretation means 30 and determination means 40 may be combinedinto one combined means or apparatus.

The stand-alone device further includes a first memory 60 that storesmodulation data including a modulating data series or curve preferablyrepresenting a time of day curve. The stand-alone device furtherincludes a second memory 50 that holds data for the creation of a dataseries or a curve representing cognitive performance capacity C overtime t. The first memory 60 and the second memory 50 may be any memoryor storage method known to those of ordinary skill in the art. Thesecond memory 50 is preferably a first-in-first-out memory providingmeans for adding the value from the determination means 40 to the end ofthe data series or the curve. The first memory and the second memory maybe combined as one memory unit. As one of ordinary skill in the art willrealize there may be a memory to store the various intermediary valuesnecessary for calculating cognitive performance capacity C and predictedcognitive performance E as required to implement this invention aseither hardware or software.

The stand-alone device also includes, as a separate IC or in combinationwith one of the previously mentioned ICs, a modulator (modulation means70) embodying S8-S9 shown in FIG. 4(b). Modulation means 70 receives thepresent cognitive performance capacity calculated by determination means40 and calculates the time of day value from data stored in the firstmemory 60. Modulation means 70 modulates the first data series or curve(cognitive performance capacity) with the time of day value. Themodulation preferably is performed by matching the timing sequenceinformation relating to the data series or the curves based on thelatter of midnight and the length of time from the initial input of dataas preferably determined by the number of epochs and the initialstarting time related to the first entered sleep/wake state. Modulationmeans 70 may modulate a series of cognitive performance capacity valueswith the time of day function if the second memory 50 exists to storethe cognitive performance capacity values.

As is well known by one of ordinary skill in the art, a counter or othersimilar functioning device and/or software coding may be used in thestand-alone device to implement S11 shown in FIG. 4(b).

The stand-alone device may also include a display to show a plottedmodulated curve representing the modulation result over time, as storedin a memory, e.g. a first-in-first-out memory, or a numericalrepresentation of a point on the modulated curve at a selected time fromthe modulation means 70 representing the predicted cognitive performanceE. The numerical representation may take the form of a gauge similar toa fuel gauge in a motor vehicle or the number itself. The stand-alonedevice, as an alternative or in addition to the display, may include aprinter or communication port to communicate with an external device forprinting and/or storage of a representation of predicted cognitiveperformance E.

The stand-alone device instead of having dedicated hardware may providethe storage space and processing power to execute a software program andaccompanying data files. In this case, the stand-alone device may be adesktop computer, a notebook computer, or similar computing device. Thesoftware program handles the receiving of the data representing sleephistory from an outside source through a communication port or via acomputer network such as intranets and the Internet, and then performsthe necessary analysis and processing of the method described herein.The storage space may be a memory in the form of computer readablematerial storing at least the time of day curve and possibly the inputdata, which may also be resident in the random-access-memory (RAM) ofthe computer given its temporary use. The input data and the resultingproduced data indicating various cognitive performance levels of anindividual may also be saved to a more permanent memory or storage thanis available in RAM.

An alternative embodiment modifies the input port 20 to receive someform of raw data, i.e., prior to being sleep scored, representing sleepactivity of an individual. In this embodiment, the interpretation means30 would then sleep score the raw data as part of the data analysisperformed by it. A third memory to store the weighting factors requiredfor sleep scoring, if a table is used for them, else the sleep scoringfunction will implicitly include the weighting factors and the thirdmemory will be unnecessary.

Another alternative embodiment provides for the interpretation means 30to filter the sleep/wake data such that for the first k_(d) number ofsleep epochs after a wake epoch are changed to wake epochs. In keepingwith the invention, the filtering may be accomplished a variety of ways.The preferred way is to add a decision step prior to S3 in FIG. 4(a)such that if D_(sw) is a sleep epoch and t−t_(LS)≦k_(d), then S3-S6 awill be skipped and S7 a will occur. The result is that the decisionrule represented as d(t) in Equation 6 above would be eliminated, and S6b and S7 d would be unnecessary in FIGS. 4(b) and 8(b).

One of ordinary skill in the art will appreciate that the stand-alonedevice is broad enough to cover a computer/workstation connected to theInternet or other computer network. A user would transmit theirsleep/wake history over such network to the stand-alone device forobtaining a predicted cognitive performance based on the transmitteddata. The interface of the stand-alone device may allow the user toadjust the variables discussed above in connection with the method tolearn the interrelationship between the variables and the predictedcognitive performance. Preferably, the range of allowable adjustment ofthe variables would be that of the respective ranges discussed inconnection with each of the variables above.

The component add-on to the measuring device may have similar componentsto the stand-alone device described above and shown in FIG. 7(a).Preferably the component add-on is contained in one integrated chip tominimize the space needed to house it and/or is implemented as softwareas part of a designed measuring device. The input means 20 becoming, forexample, a wire or other type of connector. However, the add-oncomponent may include more than one electrical component, e.g., a memorychip and an IC. The component add-on may transmit the predictedcognitive performance to a remote device for further analysis.

The apparatus for accomplishing the third step is illustrated as part ofFIG. 7(b). The additional components preferably include a task inputmeans 20′ for receiving information regarding the task that may eitherbe manually provided through some sort of data entry mechanism such as akeyboard, touch pad, a button or set of buttons, a touch screen or othersimilar mechanisms, or through analysis of the data collected by theattached device. Alternatively, the task input means 20′ may be a partof or similar to the input means 20. Preferably, a determination means40′ for calculating the task modulator based on what is received fromthe task input means 20′. The determination means 40′ preferably is incommunication with the modulation means 70′, which is the modulationmeans 70 with the added modulation of the task modulator. As with thedevice described in connection with FIG. 7(a), the various components ofFIG. 7(b) may be consolidated into one or a series of combinationcomponents. Additionally, the components in FIGS. 7(a) and 7(b) may alsonot be directly connected but separated into different devices.

The alternative embodiment described above involving real-timeadaptation of the above-described method may be implemented by theaddition of a routine such that the individual is notified to press abutton for recalibration on the recording device. Based upon theindividual's response time, the individual's current level of cognitiveperformance is determined and adjustments are accordingly made to theabovedescribed Equations to allow for the recent activity of theindividual to lead to the determined cognitive performance.

Both the software and/or hardware are envisioned as being able tooperate and function in real-time. For the purposes of this invention,real-time is understood to represent a continuous stream and analysis ofcognitive performance level for each epoch of sleep/wake data entered.Thus, the software and/or hardware will both provide to an individual orsome other entity the present cognitive performance level based on thedata from the last entered epoch of sleep/wake data entered into eitherthe software or hardware. Most sleep scoring systems make the sleep/wakedetermination based on data from epochs on either side of the epochbeing analyzed. Consequently, there would be a delay in providinginformation to the user.

As one of ordinary skill in the art will appreciate from thisdescription, the described method is able to accept a continuous streamof data from either individual epochs or groups of epochs. If block_(s)of time are entered, then after initial transitions the first few epochsare governed by the appropriate transition function with the appropriatetime of solid sleep or wakefulness being used in the non-transitionfunctions.

As a feature of the invention, the sleep/wake data may comprise the timeat which a state change occurs from sleep to wake or wake to sleep. Thesleep/wake data may also comprise the duration of the individual's wakestate and the duration of the individual's sleep state. In order togenerate the predicted cognitive performance curve, the sleep/wake datamay be extrapolated and/or expanded into a series of individual epochs.As discussed above an epoch represents a predetermined length of time.Thus the sleep/wake data may be presented in conventional units of timeor may be presented in epochs. For example, if the sleep/wake data wassleep for 10 epochs and wake for 3 epochs, in generating the cognitiveperformance capacities, epochs 1 through 10 may represent the sleepstate and epochs 11 through 13 may represent the wake state.

In accordance with an aspect of the invention, the predicted cognitiveperformance E at a particular time q may be determined using either thepredicted cognitive performance E or the cognitive performance capacityC at time r as a base point where r can be before or after time q. Fromthe base point determining the cognitive performance capacities for thetime points between times q and r where there is a change in state.

As shown in FIGS. 8(a)-(b), the steps are substantially the same as thepreferred embodiment with changes made to the wake and sleep functions,consequently the definition of the variables is the same as thepreferred embodiment except as noted. The equations described below andthe steps shown in FIGS. 8(a)-(b) are for the situation when the initialcognitive value is prior in time to the desired predicted cognitivevalue. Each element of sleep/wake data is classified as either sleep orwake.

If the sleep/wake data represents the wake state, then the impact of thetask function t(t) is determined. Alternatively, the task function t(t)may be modulated by the time of day function M prior to modulating thewake function w_(m)(t) or the sleep inertia function i(t). Next, aselection is made between two functions as to which is applicable basedon the following decision rule:

IFΔt≦k_(i)

THEN C _(t) =i(t)

ELSE C _(t) =w _(m)(t)  Equation 13

where Δt represents the amount of time in the current state, i.e.,t−t_(LS). The sleep inertia function i(t) is used only if the last dataentry is the wake state for a period of time is less than or equal tok_(i). Thus the same sleep inertia function i(t) as used in thepreferred embodiment is also used in this alternative embodiment afterbeing modulated by the task function t(t). The modified wake functionw_(m)(t) takes into account that the sleep inertia function i(t)provides a delay of k_(i) when a curve is formulated, such that after anindividual recovers from the initial suppression of cognitiveperformance capacity the individual returns to the level of cognitiveperformance capacity of the last epoch the individual was asleep priorto waking. Accounting for this delay provides the following:

w _(m)(t)=C _(t−1) −k _(w)(Δt−k _(i))  Equation 14

Alternatively, the modified wake function w_(m)(t) may begin at a pointwhere an undelayed w_(m)(t) intersects the sleep inertia function i(t).The wake function w_(m)(t) is modulated by the task function t(t) undereither alternative.

If the sleep/wake data represents the sleep state, then a selection ismade between two functions as to which is applicable based on thefollowing decision rule:

IF Δt≦k_(d)

THEN C _(t) =d(t)

ELSE C _(t) =s _(m)(t)  Equation 15

The delay function d(t) is used only if the last data entry is the sleepstate for a period of time is less than or equal to k_(d). Thus the samedelay function d(t) as used in the preferred embodiment is also used inthis alternative embodiment. The modified sleep function s_(m)(t) takesinto account the delay function for a period of time equal to k_(d).Accounting for the delay function d(t) provides the following:

s _(m)(t)=((C _(t−1)−(k _(w) * k _(d)))+(100−(100−C _(t−1)) (1−1/k_(s))^(Δt−kd)))  Equation 16

where the first part of the equation represents the delay function d(t)and the second part represents the recovery of cognitive performancecapacity C (f(t) portion of S7 c′).

A summation of the time components of the sleep/wake data is performedas each piece of sleep/wake data is handled with respect to thecalculation of the cognitive performance capacity or prior to modulationof the final cognitive performance capacity with the time of dayfunction m(t). The latter is shown in FIGS. 8(a)-(b). After the newcognitive performance capacity C_(t) is calculated, the method repeatsto handle the next piece of sleep/wake data if the present piece is notthe last piece. After the last piece the predicted cognitive performanceE is calculated based on Equation 1 above and as detailed in thepreferred embodiment.

Alternatively, the task function t(t) may be included at the same timeof the time of day function m(t) instead of for each set of wake statesby moving S9 b through S10 b to a position similar to that illustratedin FIG. 4(b).

It should be noted again that this method includes the processes andcalculations based on Equations 1 through 12 expressed in their generalform, with an alternative being the removal of the task functionelements. Embodiments shall apply functions relating the variablesinvolved according to empirical knowledge, resulting in specificexpressions of those equations, as illustrated in the text and FIGS.1-4(b) above (but not confined to these), which may be changed orrefined according to the state of empirical knowledge.

Applications of the Invention

There are a variety of potential applications of this invention. In itssimplest application, the method according to the invention may be usedto predict the impact of various idealized (i.e., unfragmented) amountsof nightly sleep on predicted cognitive performance E. Another practicalapplication uses the method to predict the cognitive performance in anindividual with fragmented sleep, either due to a sleep disorder such assleep apnea or due to environmental disturbances such as airplane ortrain noises. Another practical application uses the method to predictthe cognitive performance E of an individual changing his/her schedulefor night shift work.

In another application, the method is used to retrospectively predictcognitive performance E in a commercial motor vehicle operator involvedin a driving collision/traffic accident. In this application, the methodis used first to predict an individual's level of cognitive performanceE across some interval based on that individual's current work andsleep/wake schedule.

Another similar application is using the method to re-schedule sleep andwakefulness in order to optimize predicted cognitive performance E overan interval for a commercial motor vehicle operator. In this example,first we model a driver's predicted cognitive performance E based on hiscurrent sleep/wake schedule. The driver's current sleep/wake schedule isgenerated around the maximum duty hours allowed under the FederalHighway Administration's (FHWA) hours-of-service regulations. Theseregulations allow the driver to obtain a maximum 15 hours on-duty(maximum 10 hours driving plus five hours on-duty but not driving)followed by a minimum eight hours off-duty. The driver may continue thison/off-duty cycling until 60 hours on-duty has been accumulated—at whichpoint the driver must take time off until seven days has elapsed sincehe commenced duty. An alternative work schedule also allowed undercurrent FHWA regulations is based on a schedule of 12 hours on-duty and12 hours off-duty with the underlying assumption that the driver sleepseight of his 12 hours off-duty. The use of this invention will allow theselection of the schedule that allows for maximizing the driver'scognitive performance levels throughout a period of time.

Although described above in connection with a variety of specificactivities, this invention has many other applications. The method forpredicting cognitive performance will provide critical information formanaging both individual and group productivity. For example, inmilitary operational planning, this method will enable commanders todetermine precisely, based on prior sleep history and duties performed,each soldier's current and predicted level of cognitive performance.Commanders can also input a likely sleep/wake and work schedules andthereby predict a soldier's cognitive performance throughout animpending mission. Throughout conduct of the mission itself, the lattercognitive performance predictions (originally based on likely sleep/wakeand work schedules) can be updated based on actual sleep acquired andwork performed by an individual soldier. The ability to project futurecognitive performance will allow commanders to optimize troopperformance during continuous operations by, for example, planningsleep/wake and duty schedules around the mission to optimize cognitiveperformance, selecting those troops or combinations of troops whosepredicted cognitive performance will be maximal at a critical time, etc.This method will assist in maximizing productivity at both theindividual level and unit level.

This invention may be employed in a variety of commercial applicationscovering many occupational areas for purposes of optimizing output(productivity). The invention provides managers with the capability toplan operations and regulate work hours to a standard based on objectivecognitive performance predictions. This is in contrast to the frequentlyused method of regulating work hours by time off-duty (a relatively poorpredictor of sleep/wake patterns and performance of tasks, andconsequently a poor predictor of cognitive performance) or by generatingalertness/sleepiness predictions (which, as noted above, do not alwayscorrespond to cognitive performance). The invention can be “exercised”in hypothetical sleep/wake and duty scenarios to provide an estimate ofcognitive performance under such scenarios. To the extent thatoptimizing cognitive performance is of interest to the general public,there is a possibility for use in a variety of applications.

This invention also may be used in conjunction with drugs to alter thesleep/wake cycle of an individual and/or optimize or minimize thecognitive performance level of an individual as needed and/or desired.

This invention also can work conjunctionally with the concepts ofparticle swarm theory/algorithms. Particle swarm algorithms areroutinely used to optimize the throughput of containers through a shipport or to optimize the use of workers within a work group to performtasks over a given period. An example of an application is the planningof a mission for an army unit by its commander.

The method may also be used to gauge and evaluate the cognitiveperformance effects of any biomedical, psychological, or other (e.g.,sleep hygiene, light therapy, etc.) treatments or interventions shown toimprove sleep. Examples of these include but are not limited to patientswith overt sleep disorders, circadian rhythm disorders, other medicalconditions impacting sleep quality and/or duration, poor sleep hygiene,jet lag, or any other sleep/wake problem. Currently, the efficacy oftreatments for improving sleep is determined by comparing baselinepolysomnographic measures of nighttime sleep and some measure of daytimealertness (e.g., the MSLT, the Maintenance of Wakefulness Test (MWT),the Stanford Sleepiness Scale or the Karolinska Sleepiness Scale) withthe same measures obtained after treatment. Both treatment efficacy andthe likely impact on performance during waking periods are inferred fromthe results on the daytime alertness tests. For example, the FederalAviation Administration currently requires any commercial pilotsdiagnosed with sleep apnea to undergo treatment. Such treatment isfollowed by daytime alertness testing on a modified version of the MWT.During the MWT, pilots are put in a comfortable chair in a darkened roomand instructed to try to remain awake for extended periods. If thepilots are able to avoid overt sleep under these sleep-conduciveconditions then they are deemed fit for duty. The inference is that theminimal ability to maintain wakefulness at a discrete point in timetranslates into the ability to operate an aircraft safely (i.e., it isinferred that alertness is equivalent to cognitive performance).However, sleep deprivation can affect cognitive performance even when itdoes not result in overt sleep, particularly during an alertness testwhen for various reasons the individual may be highly motivated to stayawake.

In contrast, the current method allows cognitive performance to beestimated directly from measured sleep parameters considered inconjunction with the time of day and performance of tasks. Theadvantages of this method over current methods for evaluating treatmentefficacy are: (1) the motivations and motivation levels of the patientsbeing tested cannot affect results (cognitive performancedeterminations); and (2) the method allows numerical specification andprediction of cognitive performance across all projected waking hoursrather than indicating alertness at a discrete, specified point in time.Thus, the method provides a continuous scale for gauging cognitiveperformance across time rather than providing only a minimal “fitnessfor duty” determination based on the patient's ability to maintainEEG-defined wakefulness at a specific time.

The method may also be used clinically as an adjunct for diagnosingsleep disorders such as narcolepsy and idiopathic CNS hypersomnolence.Equally important, it may also be used to differentiate among sleepdisorders. The latter is critical to the course of treatment, andconsequent treatment efficacy depends on a valid and reliable diagnosis.For example, sleep apnea and periodic limb movements during sleep arecharacterized by nighttime sleep disruption (i.e., partial sleepdeprivation) accompanied by daytime cognitive performance deficits. Incontrast, narcolepsy and idiopathic hypersomnolence tend to becharacterized by apparently normal nighttime sleep, but accompanied bydaytime cognitive performance deficits. Based on the apparently normalnighttime sleep in the latter two groups, the invention would predictrelatively normal cognitive performance. Thus, a discrepancy betweenpredicted cognitive performance (based on the current invention) andobserved or measured cognitive performance could be used to distinguishone sleep disorder from another. For example, narcolepsy, idiopathichypersomnolence, or other CNS-related causes of daytime cognitiveperformance deficits (where no sleep deficit is apparent) could bedistinguished from sleep apnea, periodic limb movements, or other causesof daytime cognitive deficits (where impaired sleep is evident).

Although the present invention has been described in terms of particularpreferred embodiments, it is not limited to those embodiments.Alternative embodiments, examples, and modifications which would stillbe encompassed by the invention may be made by those skilled in the art,particularly in light of the foregoing teachings.

Furthermore, those skilled in the art will appreciate that variousadaptations and modifications of the above-described preferredembodiments can be configured without departing from the scope andspirit of the invention. Therefore, it is to be understood that, withinthe scope of the appended claims, the invention may be practiced otherthan as specifically described herein.

We claim:
 1. A method for providing a cognitive performance levelcomprising: receiving a data series representing at least one wake stateand at least one sleep state, selecting a function based on the dataseries, determining a cognitive performance capacity using the selectedfunction, modulating the cognitive performance capacity with a time ofday value, and providing the modulated value.
 2. The method according toclaim 1, further comprising repeating the selecting, determining, andmodulating steps for at least two pieces of the data series.
 3. Themethod according to claim 2, wherein the providing step includesdisplaying the modulated value to an individual located proximate towhere the data series is received.
 4. The method according to claim 1,wherein the time of day value is selected from a series of time of dayvalues representing a curve having a period of 24 hours.
 5. The methodaccording to claim 1, wherein the selecting step selects the functionfrom a group consisting of a wake function, a sleep function, and asleep inertia function.
 6. The method according to claim 1, wherein theselecting step selects the function from a group consisting of a wakefunction, a sleep function, a delay function, and a sleep inertiafunction.
 7. The method according to claim 1, further comprising:storing the predicted cognitive performance level, and repeating theselecting, determining, modulating, and storing steps at least once. 8.The method according to claim 7, further comprising plotting a curvebased on the stored predicted cognitive performance levels.
 9. Anapparatus for providing a cognitive performance level comprising: meansfor receiving a data series having at least one wake state and at leastone sleep state, means for selecting a function based on the dataseries, means for determining a cognitive performance capacity using theselected function, means for storing a series of time of day values,means for modulating the cognitive performance capacity with acorresponding time of day value, and means for providing the modulatedvalue.
 10. The apparatus according to claim 9, wherein the selectingmeans selects the function from a group consisting of a wake function, asleep function, and a sleep inertia function.
 11. The apparatusaccording to claim 9, wherein the selecting means selects the functionfrom a group consisting of a wake function, a sleep function, a delayfunction, and a sleep inertia function.
 12. The apparatus according toclaim 9, wherein the stored time of day values represent a curve havinga period of 24 hours.
 13. A method for determining a cognitiveperformance level comprising: inputting a data series having wake statesand sleep states of an individual, selecting a function based on thewake states and sleep states in the data series, calculating a cognitiveperformance capacity based on the selected function, modulating thecognitive performance capacity with a time of day value, and outputtingthe modulated value as the predicted cognitive performance.
 14. Themethod according to claim 13 further comprising: storing the predictedcognitive performance, repeating the selecting, calculating, modulatingand outputting steps of claim 13, plotting a curve from the storedmodulated values, and outputting the curve representing cognitiveperformance level over time.
 15. The method according to claim 14,wherein the data series includes past information such that the curve isused to determine the cognitive level of an individual at an earliertime.
 16. The method according to claim 14, further comprisingextrapolating from the curve a predictive curve based on anticipatedwake states and anticipated sleep states.
 17. The method according toclaim 13, wherein said outputting step includes outputting the predictedcognitive performance to a display.
 18. The method according to claim13, wherein said outputting step includes outputting the predictedcognitive performance to a data file.
 19. The method according to claim13, wherein said outputting step includes outputting the predictedcognitive performance to a printing device.
 20. The method according toclaim 13, further comprising formulating the time of day values torepresent a curve having a period of 24 hours.
 21. The method accordingto claim 20, wherein the curve includes a first sinusoidal curve havinga 24-hour period and a second sinusoidal curve having a 12-hour period.22. The method according to claim 13, wherein the time of day valuesrepresent a curve having a period of 24 hours.
 23. The method accordingto claim 13, wherein the data series is obtained from a device attachedto the individual.
 24. The method according to claim 13, wherein thedata series is an output of a sleep scoring system.
 25. The methodaccording to claim 13, wherein the selecting step selects from a groupconsisting of a wake function, a sleep function, a delay function, and asleep inertia function.
 26. The method according to claim 13, whereinthe selecting step selects from a group consisting of a wake function, asleep function, and a sleep inertia function.
 27. The method accordingto claim 13, wherein the selecting step includes determining the presentstate for the data series as either a wake state or a sleep state,calculating a length of time in the present state, and selecting thefunction based on the length of time in the present state and thepresent state.
 28. The method according to claim 13, wherein the firstcalculating step calculates a cognitive performance level as apercentage value such that 100% is a maximum cognitive performancecapacity.
 29. A method for determining a predicted cognitive performancecomprising: inputting a data series having wake states and sleep statesof an individual, selecting a function based on the wake states andsleep states in the data series, calculating a cognitive performancecapacity based on the selected function, approximating a first curve ofcalculated cognitive performance capacities, modulating the first curvewith a second curve representing time of day rhythms, and outputting themodulated first curve representing the predicted cognitive performance.30. The method according to claim 29, wherein said outputting stepincludes outputting a value of a point on the modulated first curve to adisplay.
 31. The method according to claim 29, wherein said outputtingstep includes outputting a value of a point on the modulated first curveto a data file.
 32. The method according to claim 29, wherein saidoutputting step includes outputting a value of a point on the modulatedfirst curve to a printing device.
 33. The method according to claim 29,further comprising extrapolating from the modulated first curve apredictive curve based on anticipated wake states and anticipated sleepstates.
 34. The method according to claim 29, further comprisingformulating the second curve having a period of 24 hours such that thesecond curve includes a first sinusoidal curve having a 24-hour periodand a second sinusoidal curve having a 12-hour period.
 35. The methodaccording to claim 29, wherein the second curve has a period of 24 hourssuch that the second curve includes a first sinusoidal curve having a24-hour period and a second sinusoidal curve having a 12-hour period.36. The method according to claim 29, wherein the data series isobtained from a device attached to the individual.
 37. The methodaccording to claim 29, wherein the data series is an output of a sleepscoring system.
 38. The method according to claim 29, wherein theselecting step selects from a group consisting of a wake function, asleep function, a delay function, and a sleep inertia function.
 39. Themethod according to claim 29, wherein the selecting step includes:determining the present state for the data series as either the wakestate or the sleep state, calculating a length of time in the presentstate, and selecting the function based on the length of time in thepresent state and the present state.
 40. The method according to claim29, wherein the calculating step includes calculating a cognitiveperformance level as a percentage value such that 100% is a maximumcognitive performance capacity.